arXiv subjects

Vincent Lenders

Publications and source records attributed to Vincent Lenders.

At least 19 recordsLinked to original sources

arXiv2026

WhiteNet: Robust Identification of Overlapping IEEE 802.11 Signals Across Unseen Channels

Deep learning (DL) classifiers trained on I/Q samples achieve high accuracy for IEEE 802.11 protocol identification of overlapping signals, but their performance degrades sharply when channel conditions at deployment differ from those encountered during training. We present WhiteNet, a framework that addresses the problem of channel variability in I/Q samples. The central idea is spectral whitening, a physics-grounded preprocessing step that suppresses frequency-selective fading while preserving protocol-discriminative features. To reduce dependence on costly multi-transmitter over-the-air captures for training, we complement it with a synthetic overlap mixer featuring a physically accurate per-transmitter channel and shared-receiver signal chain for pre-training without extensive field data collection. On public over-the-air IEEE 802.11 data, WhiteNet closes a substantial portion of the accuracy gap caused by unseen channel conditions while using 7.7 times fewer parameters than the prior state of the art, and optionally distills to compact variants for coarse spectrum awareness on power-constrained edge devices.

cs.CR↗

arXiv2026

A Framework for Using and Evaluating LLMs as Surrogate Experts in Security Surveys: Reliability, Bias, and Implications

Expert surveys are widely used in security research to study practitioner workows and decision-making, yet recruiting domain experts - especially in Security Operations Centres (SOCs), where analysts face high workload, burnout and confidentiality constraints - is difficult and often results in small samples. Large language models (LLMs) oer an appealing alternative by generating synthetic responses at scale, but little guidance exists on when such surrogate participants are reliable. We present a methodological framework for evaluating LLMs as substitutes or supplements to expert survey respondents. Using responses from SOC professionals, we compare persona-based and aggregate LLM-generated answers across multiple models and prompting settings. We measure stability, inter-model agreement and alignment with human responses. Our results show that although LLMs produce internally consistent answers, they systematically diverge from experts, exhibiting reduced variance, central tendency bias and homogenised opinions. This work contributes methodological evidence and practical guidance to the security research community on the appropriate use and limitations of LLM-generated survey responses. We conclude that LLMs are useful for piloting and hypothesis generation but not for replacing expert elicitation, and we discuss implications for researchers using LLM-augmented surveys.

cs.CY↗

arXiv2026

CSI Simulation: Why Additive Noise Fails and How to Fix It

Channel State Information (CSI) has become a widely used wireless channel sensing modality for applications such as indoor localization, activity recognition, and respiration monitoring. Because collecting labeled data under every target condition is impractical, training CSI-based models often relies on simulated data produced by adding noise or perturbations to recorded channel estimates, most commonly additive white Gaussian noise (AWGN). This practice assumes that the receiver chain between the antenna and the channel estimator is linear and gain-invariant. We test this assumption empirically using RF jamming as a controlled perturbation on 6 commodity receivers across 2 indoor environments. The assumption does not hold. Automatic gain control compresses the channel estimate multiplicatively before digitization, producing amplitude distributions that no additive noise variance can reproduce. To close the resulting fidelity gap, we propose M_QTC, a measurement-calibrated model that learns the per-subcarrier distribution transformation through quantile mapping, temporal filtering, and copula-based cross-subcarrier reordering. M_QTC reduces amplitude error 8-fold and closes 89% of the aggregate fidelity gap across four complementary dimensions. The improvement transfers directly to downstream tasks, where 5 classifiers from different families trained on M_QTC-simulated data recover 93% of real-data jamming detection performance, while AWGN-trained classifiers remain near random decision.

cs.NI↗

arXiv2026

CITADEL: CSI-Based Jamming Detection and Open-Set Classification for IIoT Networks

Radio frequency jamming poses a critical threat to the availability of wireless Industrial Internet of Things (IIoT) networks. Existing detection and classification techniques are poorly suited to this setting: coarse signal-strength and cross-layer features lack information richness, while raw I/Q baseband approaches require hardware and throughput that is impractical at the scale of hundred-node IIoT deployments. This paper presents CITADEL, a lightweight two-stage hierarchical pipeline that uses only Channel State Information (CSI) measurements, which are natively available on commodity IIoT devices, to detect and classify jamming attacks including previously unseen ones. While prior work has shown that jamming leaves observable CSI signatures, CITADEL is the first system to translate this insight into an end-to-end pipeline that jointly achieves closed-set classification of known attacks, open-set detection of zero-day attacks, and resistance to adversarial evasion. Evaluated across 6 known attack types and 15 zero-day scenarios, CITADEL achieves 100% known-attack detection and 97.1% zero-day detection at a 0.4% end-to-end false positive rate. Under adversarial evaluation spanning white-box and black-box threat models, gradient-based evasion remains below 2% across all tested perturbation budgets and the strongest published CSI attack generator achieves less than 5% average evasion. A systematic comparison against eight baselines confirms that no existing method achieves comparable performance on CSI data across all three axes: detection, generalization, and robustness. The full pipeline completes inference in 14.2 ms at 95.9 mJ on an edge GPU, establishing CITADEL as a practical solution for large-scale IIoT network security.

cs.CR↗

arXiv2026

Systematic Security Analysis of the Iridium Satellite Radio Link

The Iridium Low Earth Orbit (LEO) satellite constellation remains a unique provider of global communications for critical industries, governments, and private users, serving over 2.5 million active subscribers despite recent market competition. In contrast to terrestrial wireless standards such as 3GPP, Iridium protocol specifications are proprietary and have not undergone rigorous, public, and systematic security evaluation. In this work, we present the first comprehensive security analysis of Iridium authentication and radio link protocols. We reverse engineer Iridium SIM-based authentication mechanism and demonstrate that the secret key can be extracted from the SIM card, enabling full device cloning and impersonation attacks. Leveraging a month-long dataset of Iridium up- and downlink satellite traffic, we further show that nearly all signaling and radio communication protocols currently in use lack encryption, resulting in the exposure of sensitive information in cleartext over the air such as login credentials and large volumes of personal data. Finally, we develop custom software-defined radio (SDR) tools to carry out spoofing and jamming attacks, revealing that modestly equipped adversaries can inject falsified messages or disrupt the Iridium service locally due to the absence of source authentication. Our findings uncover systemic vulnerabilities in the Iridium radio link and highlight the urgent need for users of critical applications to transition to more secure communication radio links.

cs.CR↗

arXiv2026

Practical Challenges in Executing Shor's Algorithm on Existing Quantum Platforms

Quantum computers pose a fundamental threat to widely deployed public-key cryptosystems, such as RSA and ECC, by enabling efficient integer factorization using Shor's algorithm. Theoretical resource estimates suggest that 2048-bit RSA keys could be broken using Shor's algorithm with fewer than a million noisy qubits. Although such machines do not yet exist, the availability of smaller, cloud-accessible quantum processors and open-source implementations of Shor's algorithm raises the question of what key sizes can realistically be factored with today's platforms. In this work, we experimentally investigate Shor's algorithm on several cloud-based quantum computers using publicly available implementations. Our results reveal a substantial gap between the capabilities of current quantum hardware and the requirements for factoring cryptographically relevant integers. In particular, we observe that circuit constructions still need to be highly specific for each modulus, and that machine fidelities are unstable, with high and fluctuating error rates.

quant-ph↗

arXiv2025

OpenSky Report 2025: Improving Crowdsourced Flight Trajectories with ADS-C Data

The OpenSky Network has been collecting and providing crowdsourced air traffic surveillance data since 2013. The network has primarily focused on Automatic Dependent Surveillance--Broadcast (ADS-B) data, which provides high-frequency position updates over terrestrial areas. However, the ADS-B signals are limited over oceans and remote regions, where ground-based receivers are scarce. To address these coverage gaps, the OpenSky Network has begun incorporating data from the Automatic Dependent Surveillance--Contract (ADS-C) system, which uses satellite communication to track aircraft positions over oceanic regions and remote areas. In this paper, we analyze a dataset of over 720,000 ADS-C messages collected in 2024 from around 2,600 unique aircraft via the Alphasat satellite, covering Europe, Africa, and parts of the Atlantic Ocean. We present our approach to combining ADS-B and ADS-C data to construct detailed long-haul flight paths, particularly for transatlantic and African routes. Our findings demonstrate that this integration significantly improves trajectory reconstruction accuracy, allowing for better fuel consumption and emissions estimates. We illustrate how combined data captures flight patterns across previously underrepresented regions across Africa. Despite coverage limitations, this work marks an important advancement in providing open access to global flight trajectory data, enabling new research opportunities in air traffic management, environmental impact assessment, and aviation safety.

cs.SI↗

arXiv2023

FABRID: Flexible Attestation-Based Routing for Inter-Domain Networks

In its current state, the Internet does not provide end users with transparency and control regarding on-path forwarding devices. In particular, the lack of network device information reduces the trustworthiness of the forwarding path and prevents end-user applications requiring specific router capabilities from reaching their full potential. Moreover, the inability to influence the traffic's forwarding path results in applications communicating over undesired routes, while alternative paths with more desirable properties remain unusable. In this work, we present FABRID, a system that enables applications to forward traffic flexibly, potentially on multiple paths selected to comply with user-defined preferences, where information about forwarding devices is exposed and transparently attested by autonomous systems (ASes). The granularity of this information is chosen by each AS individually, protecting them from leaking sensitive network details, while the secrecy and authenticity of preferences embedded within the users' packets are protected through efficient cryptographic operations. We show the viability of FABRID by deploying it on a global SCION network test bed, and we demonstrate high throughput on commodity hardware.

cs.NI↗

arXiv2023

Lessons Learned in ATCO2: 5000 hours of Air Traffic Control Communications for Robust Automatic Speech Recognition and Understanding

Voice communication between air traffic controllers (ATCos) and pilots is critical for ensuring safe and efficient air traffic control (ATC). This task requires high levels of awareness from ATCos and can be tedious and error-prone. Recent attempts have been made to integrate artificial intelligence (AI) into ATC in order to reduce the workload of ATCos. However, the development of data-driven AI systems for ATC demands large-scale annotated datasets, which are currently lacking in the field. This paper explores the lessons learned from the ATCO2 project, a project that aimed to develop a unique platform to collect and preprocess large amounts of ATC data from airspace in real time. Audio and surveillance data were collected from publicly accessible radio frequency channels with VHF receivers owned by a community of volunteers and later uploaded to Opensky Network servers, which can be considered an "unlimited source" of data. In addition, this paper reviews previous work from ATCO2 partners, including (i) robust automatic speech recognition, (ii) natural language processing, (iii) English language identification of ATC communications, and (iv) the integration of surveillance data such as ADS-B. We believe that the pipeline developed during the ATCO2 project, along with the open-sourcing of its data, will encourage research in the ATC field. A sample of the ATCO2 corpus is available on the following website: https://www.atco2.org/data, while the full corpus can be purchased through ELDA at http://catalog.elra.info/en-us/repository/browse/ELRA-S0484. We demonstrated that ATCO2 is an appropriate dataset to develop ASR engines when little or near to no ATC in-domain data is available. For instance, with the CNN-TDNNf kaldi model, we reached the performance of as low as 17.9% and 24.9% WER on public ATC datasets which is 6.6/7.6% better than "out-of-domain" but supervised CNN-TDNNf model.

eess.AS↗

arXiv2023

Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense

Generative Language Models gained significant attention in late 2022 / early 2023, notably with the introduction of models refined to act consistently with users' expectations of interactions with AI (conversational models). Arguably the focal point of public attention has been such a refinement of the GPT3 model -- the ChatGPT and its subsequent integration with auxiliary capabilities, including search as part of Microsoft Bing. Despite extensive prior research invested in their development, their performance and applicability to a range of daily tasks remained unclear and niche. However, their wider utilization without a requirement for technical expertise, made in large part possible through conversational fine-tuning, revealed the extent of their true capabilities in a real-world environment. This has garnered both public excitement for their potential applications and concerns about their capabilities and potential malicious uses. This review aims to provide a brief overview of the history, state of the art, and implications of Generative Language Models in terms of their principles, abilities, limitations, and future prospects -- especially in the context of cyber-defense, with a focus on the Swiss operational environment.

cs.CL↗

arXiv2022

Orchestrating Collaborative Cybersecurity: A Secure Framework for Distributed Privacy-Preserving Threat Intelligence Sharing

Cyber Threat Intelligence (CTI) sharing is an important activity to reduce information asymmetries between attackers and defenders. However, this activity presents challenges due to the tension between data sharing and confidentiality, that result in information retention often leading to a free-rider problem. Therefore, the information that is shared represents only the tip of the iceberg. Current literature assumes access to centralized databases containing all the information, but this is not always feasible, due to the aforementioned tension. This results in unbalanced or incomplete datasets, requiring the use of techniques to expand them; we show how these techniques lead to biased results and misleading performance expectations. We propose a novel framework for extracting CTI from distributed data on incidents, vulnerabilities and indicators of compromise, and demonstrate its use in several practical scenarios, in conjunction with the Malware Information Sharing Platforms (MISP). Policy implications for CTI sharing are presented and discussed. The proposed system relies on an efficient combination of privacy enhancing technologies and federated processing. This lets organizations stay in control of their CTI and minimize the risks of exposure or leakage, while enabling the benefits of sharing, more accurate and representative results, and more effective predictive and preventive defenses.

cs.CR↗

arXiv2022

Improving Aircraft Localization: Experiences and Lessons Learned from an Open Competition

Knowledge about the exact positioning of aircraft is crucial in many settings. Consequently, the opportunistic and independent localization of aircraft based on their communication has been a longstanding problem and subject of much research. Originating from military settings, the capability to conduct aircraft localization has moved first towards the institutional civil aviation domain and can now be undertaken by anyone who has access to multiple cheap software-defined radios. Based on these technological developments, many crowdsourced sensor networks have sprung up, which collect air traffic control data in order to localize aircraft and visualize the airspace. Due to their unplanned and uncontrolled deployment and heterogeneous receiver technology traditional solutions to the Aircraft Localization Problem (ALP) can either not be applied or do not perform in a satisfactory manner. In order to deal with this issue and to find novel approaches to the ALP itself, we have designed and executed a multi-stage open competition, conducted both offline and online. In this paper, we discuss the setup, experiences, and lessons learned from this Aircraft Localization Competition. We report from a diverse set of technical approaches, comprising 72 participating teams over three stages. The participants reached a localization accuracy of up to 25 meters in a setting with fully GPS-synchronized receivers and 78 meters in a largely unsynchronized receiver setting. These results constitute a significant improvement over the previous baseline used in the OpenSky research network. We compare the results of the study, discuss the current state of the art, and highlight the areas that, based on our experience from organizing a competition, need to be improved for optimal adoption of the competitive approach for other scenarios.

cs.NI↗

arXiv2021

From Scattered Sources to Comprehensive Technology Landscape: A Recommendation-based Retrieval Approach

Mapping the technology landscape is crucial for market actors to take informed investment decisions. However, given the large amount of data on the Web and its subsequent information overload, manually retrieving information is a seemingly ineffective and incomplete approach. In this work, we propose an end-to-end recommendation based retrieval approach to support automatic retrieval of technologies and their associated companies from raw Web data. This is a two-task setup involving (i) technology classification of entities extracted from company corpus, and (ii) technology and company retrieval based on classified technologies. Our proposed framework approaches the first task by leveraging DistilBERT which is a state-of-the-art language model. For the retrieval task, we introduce a recommendation-based retrieval technique to simultaneously support retrieving related companies, technologies related to a specific company and companies relevant to a technology. To evaluate these tasks, we also construct a data set that includes company documents and entities extracted from these documents together with company categories and technology labels. Experiments show that our approach is able to return 4 times more relevant companies while outperforming traditional retrieval baseline in retrieving technologies.

cs.IR↗

arXiv2021

5G System Security Analysis

Fifth generation mobile networks (5G) are currently being deployed by mobile operators around the globe. 5G acts as an enabler for various use cases and also improves the security and privacy over 4G and previous network generations. However, as recent security research has revealed, the standard still has security weaknesses that may be exploitable by attackers. In addition, the migration from 4G to 5G systems is taking place by first deploying 5G solutions in a non-standalone (NSA) manner where the first step of the 5G deployment is restricted to the new radio aspects of 5G, while the control of the user equipment is still based on 4G protocols, i.e. the core network is still the legacy 4G evolved packet core (EPC) network. As a result, many security vulnerabilities of 4G networks are still present in current 5G deployments. This paper presents a systematic risk analysis of standalone and non-standalone 5G networks. We first describe an overview of the 5G system specification and the new security features of 5G compared to 4G. Then, we define possible threats according to the STRIDE threat classification model and derive a risk matrix based on the likelihood and impact of 12 threat scenarios that affect the radio access and the network core. Finally, we discuss possible mitigations and security controls. Our analysis is generic and does not account for the specifics of particular 5G network vendors or operators. Further work is required to understand the security vulnerabilities and risks of specific 5G implementations and deployments.

cs.NI↗

arXiv2021

Blockchain in Cyberdefence: A Technology Review from a Swiss Perspective

Since the advent of bitcoin in 2008, the concept of a blockchain has widely spread. Besides crypto currencies and trading activities, there is a wide range of potential application areas where blockchains are providing the main building block for secure solutions. From a technical point of view, a blockchain involves a set of cryptographic primitives to provide a data structure with security and trust properties. However, a blockchain is not a golden bullet. It may be well suited for some problems, but often an inappropriate data structure for many applications. In this paper, we review the high-level concept of a blockchain and present possible applications in the military field. Our review is targeted to readers with little prior domain knowledge as a support to decide where it makes sense to use a blockchain and where a blockchain might not be the right tool at hand.

cs.CR↗

arXiv2020

LocaRDS: A Localization Reference Data Set

The use of wireless signals for purposes of localization enables a host of applications relating to the determination and verification of the positions of network participants, ranging from radar to satellite navigation. Consequently, it has been a longstanding interest of theoretical and practical research in mobile networks and many solutions have been proposed in the scientific literature. However, it is hard to assess the performance of these in the real world and, more severely, to compare their advantages and disadvantages in a controlled scientific manner. With this work, we attempt to improve the current state of the art in localization research and put it on a solid scientific grounding for the future. Concretely, we develop LocaRDS, an open reference dataset of real-world crowdsourced flight data featuring more than 222 million measurements from over 50 million transmissions recorded by 323 sensors. We show how LocaRDS can be used to test, analyze and directly compare different localization techniques and further demonstrate its effectiveness by examining the open question of the aircraft localization problem in crowdsourced sensor networks. Finally, we provide a working reference implementation for the aircraft localization problem and a discussion of possible metrics for use with LocaRDS.

cs.NI↗

arXiv2020

Understanding Realistic Attacks on Airborne Collision Avoidance Systems

Airborne collision avoidance systems provide an onboard safety net should normal air traffic control procedures fail to keep aircraft separated. These systems are widely deployed and have been constantly refined over the past three decades, usually in response to near misses or mid-air collisions. Recent years have seen security research increasingly focus on aviation, identifying that key wireless links---some of which are used in collision avoidance---are vulnerable to attack. In this paper, we go one step further to understand whether an attacker can remotely trigger false collision avoidance alarms. Primarily considering the next-generation Airborne Collision Avoidance System X (ACAS X), we adopt a modelling approach to extract attacker constraints from technical standards before simulating collision avoidance attacks against standardized ACAS X code. We find that in 44% of cases, an attacker can successfully trigger a collision avoidance alert which on average results in a 590 ft altitude deviation; when the aircraft is at lower altitudes, this success rate rises considerably to 79%. Furthermore, we show how our simulation approach can be used to help defend against attacks by identifying where attackers are most likely to be successful.

cs.CR↗

arXiv2020

Classi-Fly: Inferring Aircraft Categories from Open Data using Machine Learning

In recent years, air traffic communication data has become easy to access, enabling novel research in many fields. Exploiting this new data source, a wide range of applications have emerged, from weather forecasting to stock market prediction, or the collection of information about military and government movements. Typically these applications require knowledge about the metadata of the aircraft, specifically its operator and the aircraft category. armasuisse Science + Technology, the R\&D agency for the Swiss Armed Forces, has been developing Classi-Fly, a novel approach to obtain metadata about aircraft based on their movement patterns. We validate Classi-Fly using several hundred thousand flights collected through open source means, in conjunction with ground truth from publicly available aircraft registries containing more than two million aircraft. Classi-Fly obtains the correct aircraft category with an accuracy of over 88%, demonstrating that it can improve the meta data necessary for applications working with air traffic communication. Finally, we show that it is feasible to automatically detect specific flights such as police and surveillance missions.

cs.LG↗